
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    int          =			0
			__UpperCamelCase   :    bool          =			False
			__UpperCamelCase   :    float          =			3.0




class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()    ,						{}   )
							self.assertDictEqual(MockClass(a=2   ).to_kwargs()    ,						{"""a""": 2}   )
							self.assertDictEqual(MockClass(a=2    ,						b=snake_case_   ).to_kwargs()    ,						{"""a""": 2, """b""": True}   )
							self.assertDictEqual(MockClass(a=2    ,						c=2.25   ).to_kwargs()    ,						{"""a""": 2, """c""": 2.25}   )

			@require_cuda
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							UpperCamelCase_:				List[Any]						=    GradScalerKwargs(init_scale=1024    ,						growth_factor=2   )
							AcceleratorState._reset_state()
							UpperCamelCase_:				Optional[Any]						=    Accelerator(mixed_precision="""fp16"""    ,						kwargs_handlers=[scaler_handler]   )
							print(accelerator.use_fpaa   )
							UpperCamelCase_:				int						=    accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale    ,						1024.0   )
							self.assertEqual(scaler._growth_factor    ,						2.0   )

							# Check the other values are at the default
							self.assertEqual(scaler._backoff_factor    ,						0.5   )
							self.assertEqual(scaler._growth_interval    ,						2000   )
							self.assertEqual(scaler._enabled    ,						snake_case_   )




			@require_multi_gpu
			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Optional[int]						=    ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
							execute_subprocess_async(snake_case_    ,						env=os.environ.copy()   )


if __name__ == "__main__":
							lowerCamelCase_    :			List[str]    =	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
							lowerCamelCase_    :			Union[str, Any]    =	Accelerator(kwargs_handlers=[ddp_scaler])
							lowerCamelCase_    :			List[Any]    =	torch.nn.Linear(1_00, 2_00)
							lowerCamelCase_    :			Any    =	accelerator.prepare(model)

							# Check the values changed in kwargs
							lowerCamelCase_    :			List[Any]    =	""""""
							lowerCamelCase_    :			int    =	model.bucket_bytes_cap // (10_24 * 10_24)
							if observed_bucket_cap_map != 15:
														error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
							if model.find_unused_parameters is not True:
														error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

							# Check the values of the defaults
							if model.dim != 0:
														error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
							if model.broadcast_buffers is not True:
														error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
							if model.gradient_as_bucket_view is not False:
														error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






